Traditionally, metabolic and gene-regulatory networks were analysed separately. There are various tools for metabolic network reconstruction, and for the generation of gene-regulatory networks, but still there rarely exist tools combining both networks. This poor connectivity between the two outlined approaches might be due to the fact that the required information is stored in different databases. Information on transcription factor binding sites can be found for example in RegulonDB or PRODORIC, while metabolic reactions or pathways need to be retrieved from other database. Combining knowledge from multiple disciplines, however, will drive our understanding of cellular processes and lead to the prediction of the cellular behaviour in its entirety.

SYSTOMONAS follows the notion towards systems biology by providing a broad range of information: protein, gene and homolog data, metabolome experiment results as well as metabolic networks extended with gene-regulatory networks. Several facilities for visualization and analysis are provided or linked enabling a quick understanding of metabolic pathways, gene neighbourhood or further transcription binding sites. Here we focus on data integration for the biotechnologically and medically relevant group of bacteria, the pseudomonads, and in particular Pseudomonas aeruginosa, an opportunistic human pathogen (see section 'About' for details). These data provide a suitable platform for systems biology analyses, i.e. modelling and simulation of metabolic networks and its transcriptional regulation. With this database we would like to encourage the Pseudomonas community to elucidate cellular processes of interest using an integrated systems biology strategy.

Without these databases SYSTOMONAS would not be existing:

Data from these external databases are liable to copyright regulation of the corresponding database.

Pseudomonas aeruginosa is a Gram-negative bacterium and an important opportunistic human pathogen. It is characterized by its intrinsic resistance to antibiotics and causes persistent infections in immunocompromized patients. Particularly cystic fibrosis patients suffer from persistent lung infections by P. aeruginosa. Besides the medically relevant P. aeruginosa the genera Pseudomonas contains various important plant pathogens and biotechnologically as well as ecologically interesting species.

We use a systems biology approach to get a deeper understanding of all cellular processes of P. aeruginosa during infection. Our long term goal is the development of a dynamic model simulating P. aeruginosa during infection. The basis for such an approach is SYSTOMONAS, a comprehensive database that includes systems data from all levels of analysis as microarray and proteomics data, metabolite measurements, sequence data, gene-regulatory networks and enzyme data. Therefore, we started with metabolomics analysis and extended to transcriptomics, genomics, and proteomics aspects. Along with the wet lab results additional data is stored, which is extracted from literature or derived from other external databases. Major sources of SYSTOMONAS are KEGG, PRODORIC, BRENDA (see section 'Sources'), which are partly stored via the data warehouse system and partly dynamically connected via SOAP, a platform-independent data transfer protocol.
Comparing a Pseudomonas protein of interest with other well-characterized proteins may deliver useful insights into the evolution, distribution, and species specific function. Therefore, we searched for all deduced proteins of the SYSTOMONAS database for orthologous proteins in other Pseudomonas species to obtain orthologous protein clusters.

The database PRODORIC (PROkaryotic Database Of gene Regulation) is currently the largest database about gene regulatory networks in prokaryotes. It is an integrated approach to provide information about molecular networks in prokaryotes with focus on pathogenic organisms. PRODORIC contains detailed information about operon and promoter structures including transcription factor binding sites and provides several tools for regulator binding site prediction (see website for more details).

BRENDA, the BRaunschweiger ENzyme DAtenbank, is the largest database containing detailed information about enzyme function and metabolic pathways, which is carefully extracted manually from primary literature. Features of the database include: kinetic parameters, disease information as well as cofactors are parts of BRENDA (see website for more details).

SYSTOMONAS extends and integrates BRENDA, KEGG and PRODORIC in respect of storing high-throughput data, which is emerging during this project. Other research groups are invited to insert their own data to SYSTOMONAS.

The project including this SYSTOMONAS database is funded by the German Bundesministerium für Bildung und Forschung (BMBF) and is managed by the National Genome Research Network (NGFN2-EP, grant no. 0313398A). SYSTOMONAS is maintained at the Technical University of Braunschweig in the Institute of Microbiology.

This project is sustained by the cooperation of Dietmar Schomburg's group and Dieter Jahn's. The SYSTOMONAS team consists of biologists and bioinformaticians.
An alphabetical list of current contributors is shown below. Please also visit the website at the Technical University Braunschweig.

• A global overview of SYSTOMONAS has been published (PMID: 17202169).
• Details about the database architecture and data retrieval techniques can be found at the Journal of Integrative Bioinformatics.
• Metabolome analysis is described in Klawonn et al. (2007).

We have chosen the open source object-relational database management system PostgreSQL (8.0.3, see website) for our database SYSTOMONAS. This database is accessed with the scripting language PHP (see website), which also allows the dynamic generation of the webinterface. The webserver is Apache 2.0 (see website).
Data integration with SYSTOMONAS combines two different principle concepts, the data warehouse concept and dynamic web services via SOAP. The advantage of a data warehouse is mainly its fast performance during the data retrieval. The major advantage of SOAP is its up-to-dateness. Several databases provide web services via SOAP such as the major sequence databases. Several other databases such as Atlas and BRENDA are organised as data warehouses. The API (application programming interface) is constructed by the SOAP extension of PHP.
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